Structure illumination microscopy (SIM) imposes no special requirements on the fluorescent dyes used for sample labeling, yielding resolution exceeding twice the optical diffraction limit with low phototoxicity, which is therefore very favorable for dynamic observation of live samples. However, the traditional SIM algorithm is prone to artifacts due to the high signal-to-noise ratio (SNR) requirement, and existing deep-learning SIM algorithms still have the potential to improve imaging speed. Here, we introduce a deep-learning-based video-level and high-fidelity super-resolution SIM reconstruction method, termed video-level deep-learning SIM (VDL-SIM), which has an imaging speed of up to 47 frame/s, providing a favorable observing experience for users. In addition, VDL-SIM can robustly reconstruct sample details under a low-light dose, which greatly reduces the damage to the sample during imaging. Compared with existing SIM algorithms, VDL-SIM has faster imaging speed than existing deep-learning algorithms, and higher imaging fidelity at low SNR, which is more obvious for traditional algorithms. These characteristics enable VDL-SIM to be a useful video-level super-resolution imaging alternative to conventional methods in challenging imaging conditions.

Structure illumination microscopy (SIM) imposes no special requirements on the fluorescent dyes used for sample labeling, yielding resolution exceeding twice the optical diffraction limit with low phototoxicity, which is therefore very favorable for dynamic observation of live samples. However, the traditional SIM algorithm is prone to artifacts due to the high signal-to-noise ratio (SNR) requirement, and existing deep-learning SIM algorithms still have the potential to improve imaging speed. Here, we introduce a deep-learning-based video-level and high-fidelity super-resolution SIM reconstruction method, termed video-level deep-learning SIM (VDL-SIM), which has an imaging speed of up to 47 frame/s, providing a favorable observing experience for users. In addition, VDL-SIM can robustly reconstruct sample details under a low-light dose, which greatly reduces the damage to the sample during imaging. Compared with existing SIM algorithms, VDL-SIM has faster imaging speed than existing deep-learning algorithms, and higher imaging fidelity at low SNR, which is more obvious for traditional algorithms. These characteristics enable VDL-SIM to be a useful video-level super-resolution imaging alternative to conventional methods in challenging imaging conditions.

Three-dimensional (3D) imaging is essential for understanding intricate biological and biomedical systems, yet live cell and tissue imaging applications still face challenges due to constrained imaging speed and strong scattering in turbid media. Here, we present a unique phase-modulated stimulated Raman scattering tomography (PM-SRST) technique to achieve rapid label-free 3D chemical imaging in cells and tissue. To accomplish PM-SRST, we utilize a spatial light modulator to electronically manipulate the focused Stokes beam along the needle Bessel pump beam for SRS tomography without the need for mechanical z scanning. We demonstrate the rapid 3D imaging capability of PM-SRST by real-time monitoring of 3D Brownian motion of polystyrene beads in water with 8.5 Hz volume rate, as well as the instant biochemical responses to acetic acid stimulants in MCF-7 cells. Further, combining the Bessel pump beam with a longer wavelength Stokes beam (NIR-II window) provides a superior scattering resilient ability in PM-SRST, enabling rapid tomography in deeper tissue areas. The PM-SRST technique provides ∼twofold enhancement in imaging depth in highly scattering media (e.g., polymer beads phantom and biotissue like porcine skin and brain tissue) compared with conventional point-scan SRS. We also demonstrate the rapid 3D imaging ability of PM-SRST by observing the dynamic diffusion and uptake processes of deuterium oxide molecules into plant roots. The rapid PM-SRST developed can be used to facilitate label-free 3D chemical imaging of metabolic activities and functional dynamic processes of drug delivery and therapeutics in live cells and tissue.

Three-dimensional (3D) imaging is essential for understanding intricate biological and biomedical systems, yet live cell and tissue imaging applications still face challenges due to constrained imaging speed and strong scattering in turbid media. Here, we present a unique phase-modulated stimulated Raman scattering tomography (PM-SRST) technique to achieve rapid label-free 3D chemical imaging in cells and tissue. To accomplish PM-SRST, we utilize a spatial light modulator to electronically manipulate the focused Stokes beam along the needle Bessel pump beam for SRS tomography without the need for mechanical z scanning. We demonstrate the rapid 3D imaging capability of PM-SRST by real-time monitoring of 3D Brownian motion of polystyrene beads in water with 8.5 Hz volume rate, as well as the instant biochemical responses to acetic acid stimulants in MCF-7 cells. Further, combining the Bessel pump beam with a longer wavelength Stokes beam (NIR-II window) provides a superior scattering resilient ability in PM-SRST, enabling rapid tomography in deeper tissue areas. The PM-SRST technique provides ∼twofold enhancement in imaging depth in highly scattering media (e.g., polymer beads phantom and biotissue like porcine skin and brain tissue) compared with conventional point-scan SRS. We also demonstrate the rapid 3D imaging ability of PM-SRST by observing the dynamic diffusion and uptake processes of deuterium oxide molecules into plant roots. The rapid PM-SRST developed can be used to facilitate label-free 3D chemical imaging of metabolic activities and functional dynamic processes of drug delivery and therapeutics in live cells and tissue.

Three-dimensional (3D) imaging is essential for understanding intricate biological and biomedical systems, yet live cell and tissue imaging applications still face challenges due to constrained imaging speed and strong scattering in turbid media. Here, we present a unique phase-modulated stimulated Raman scattering tomography (PM-SRST) technique to achieve rapid label-free 3D chemical imaging in cells and tissue. To accomplish PM-SRST, we utilize a spatial light modulator to electronically manipulate the focused Stokes beam along the needle Bessel pump beam for SRS tomography without the need for mechanical z scanning. We demonstrate the rapid 3D imaging capability of PM-SRST by real-time monitoring of 3D Brownian motion of polystyrene beads in water with 8.5 Hz volume rate, as well as the instant biochemical responses to acetic acid stimulants in MCF-7 cells. Further, combining the Bessel pump beam with a longer wavelength Stokes beam (NIR-II window) provides a superior scattering resilient ability in PM-SRST, enabling rapid tomography in deeper tissue areas. The PM-SRST technique provides ∼twofold enhancement in imaging depth in highly scattering media (e.g., polymer beads phantom and biotissue like porcine skin and brain tissue) compared with conventional point-scan SRS. We also demonstrate the rapid 3D imaging ability of PM-SRST by observing the dynamic diffusion and uptake processes of deuterium oxide molecules into plant roots. The rapid PM-SRST developed can be used to facilitate label-free 3D chemical imaging of metabolic activities and functional dynamic processes of drug delivery and therapeutics in live cells and tissue.

Three-dimensional (3D) imaging is essential for understanding intricate biological and biomedical systems, yet live cell and tissue imaging applications still face challenges due to constrained imaging speed and strong scattering in turbid media. Here, we present a unique phase-modulated stimulated Raman scattering tomography (PM-SRST) technique to achieve rapid label-free 3D chemical imaging in cells and tissue. To accomplish PM-SRST, we utilize a spatial light modulator to electronically manipulate the focused Stokes beam along the needle Bessel pump beam for SRS tomography without the need for mechanical z scanning. We demonstrate the rapid 3D imaging capability of PM-SRST by real-time monitoring of 3D Brownian motion of polystyrene beads in water with 8.5 Hz volume rate, as well as the instant biochemical responses to acetic acid stimulants in MCF-7 cells. Further, combining the Bessel pump beam with a longer wavelength Stokes beam (NIR-II window) provides a superior scattering resilient ability in PM-SRST, enabling rapid tomography in deeper tissue areas. The PM-SRST technique provides ∼twofold enhancement in imaging depth in highly scattering media (e.g., polymer beads phantom and biotissue like porcine skin and brain tissue) compared with conventional point-scan SRS. We also demonstrate the rapid 3D imaging ability of PM-SRST by observing the dynamic diffusion and uptake processes of deuterium oxide molecules into plant roots. The rapid PM-SRST developed can be used to facilitate label-free 3D chemical imaging of metabolic activities and functional dynamic processes of drug delivery and therapeutics in live cells and tissue.

Achieving spatiotemporal control of light at high speeds presents immense possibilities for various applications in communication, computation, metrology, and sensing. The integration of subwavelength metasurfaces and optical waveguides offers a promising approach to manipulate light across multiple degrees of freedom at high speed in compact photonic integrated circuit (PIC) devices. Here, we demonstrate a gigahertz-rate-switchable wavefront shaping by integrating metasurface, lithium niobate on insulator photonic waveguides, and electrodes within a PIC device. As proofs of concept, we showcase the generation of a focus beam with reconfigurable arbitrary polarizations, switchable focusing with lateral focal positions and focal length, orbital angular momentum light beams as well as Bessel beams. Our measurements indicate modulation speeds of up to the gigahertz rate. This integrated platform offers a versatile and efficient means of controlling the light field at high speed within a compact system, paving the way for potential applications in optical communication, computation, sensing, and imaging.

We present detailed characterization of laser-driven fusion and neutron production ( $\sim {10}^5$ /second) using 8 mJ, 40 fs laser pulses on a thin (<1 μm) D ${}_2$ O liquid sheet employing a measurement suite. At relativistic intensity ( $\sim 5\times {10}^{18}$ W/cm ${}^2$ ) and high repetition rate (1 kHz), the system produces deuterium–deuterium (D-D) fusion, allowing for consistent neutron generation. Evidence of D-D fusion neutron production is verified by a measurement suite with three independent detection systems: an EJ-309 organic scintillator with pulse-shape discrimination, a ${}^3\mathrm{He}$ proportional counter and a set of 36 bubble detectors. Time-of-flight analysis of the scintillator data shows the energy of the produced neutrons to be consistent with 2.45 MeV. Particle-in-cell simulations using the WarpX code support significant neutron production from D-D fusion events in the laser–target interaction region. This high-repetition-rate laser-driven neutron source could provide a low-cost, on-demand test bed for radiation hardening and imaging applications.

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation, since light propagation through a nonabsorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMFs) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. We propose an optical neural network architecture that performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation, since light propagation through a nonabsorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMFs) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. We propose an optical neural network architecture that performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.